Fully Distributed Learning for Deep Random Vector Functional-Link Networks

In the contemporary era, the proliferation of information technology has led to an unprecedented surge in data generation, with this data being dispersed across a multitude of mobile devices. Facing these situations and the training of deep learning model that needs great computing power support, the distributed algorithm that can carry out multi-party joint modeling has attracted everyone’s attention. The distributed training mode relieves the huge pressure of centralized model on computer computing power and communication. However, most distributed algorithms currently work in a master-slave mode, often including a central server for coordination, which to some extent will cause communication pressure, data leakage, privacy violations and other issues. To solve these problems, a decentralized fully distributed algorithm based on deep random weight neural network is proposed. The algorithm decomposes the original objective function into several sub-problems under consistency constraints, combines the decentralized average consensus (DAC) and alternating direction method of multipliers (ADMM), and achieves the goal of joint modeling and training through local calculation and communication of each node. Finally, we compare the proposed decentralized algorithm with several centralized deep neural networks with random weights, and experimental results demonstrate the effectiveness of the proposed algorithm.

Underwater Image Classification Based on EfficientnetB0 and Two-Hidden-Layer Random Vector Functional Link

The ocean plays an important role in maintaining the equilibrium of Earth’s ecology and providing humans access to a wealth of resources.To obtain a high-precision underwater image classification model,we propose a classification model that combines an EfficientnetB0 neural network and a two-hidden-layer random vector functional link network(EfficientnetB0-TRVFL).The features of underwater images were extracted using the EfficientnetB0 neural network pretrained via ImageNet,and a new fully connected layer was trained on the underwater image dataset using the transfer learning method.Transfer learning ensures the initial performance of the network and helps in the development of a high-precision classification model.Subsequently,a TRVFL was proposed to improve the classification property of the model.Net construction of the two hidden layers exhibited a high accuracy when the same hidden layer nodes were used.The parameters of the second hidden layer were obtained using a novel calculation method,which reduced the outcome error to improve the performance instability caused by the random generation of parameters of RVFL.Finally,the TRVFL classifier was used to classify features and obtain classification results.The proposed EfficientnetB0-TRVFL classification model achieved 87.28%,74.06%,and 99.59%accuracy on the MLC2008,MLC2009,and Fish-gres datasets,respectively.The best convolutional neural networks and existing methods were stacked up through box plots and Kolmogorov-Smirnov tests,respectively.The increases imply improved systematization properties in underwater image classification tasks.The image classification model offers important performance advantages and better stability compared with existing methods.

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.

Functional Link Neural Network for Predicting Crystallization Temperature of Ammonium Chloride in Air Cooler System

The air cooler is an important equipment in the petroleum refining industry.Ammonium chloride(NH4 Cl)deposition-induced corrosion is one of its main failure forms.In this study,the ammonium salt crystallization temperature is chosen as the key decision variable of NH4 Cl deposition-induced corrosion through in-depth mechanism research and experimental analysis.The functional link neural network(FLNN)is adopted as the basic algorithm for modeling because of its advantages in dealing with non-linear problems and its fast-computational ability.A hybrid FLNN attached to a small norm is built to improve the generalization performance of the model.Then,the trained model is used to predict the NH4 Cl salt crystallization temperature in the air cooler of a sour water stripper plant.Experimental results show the proposed improved FLNN algorithm can achieve better generalization performance than the PLS,the back propagation neural network,and the conventional FLNN models.

Application of functional-link neural network in evaluation of sublayer suspension based on FWD test

Several methods for evaluating the sublayer suspension beneath old pavement with falling weight deflectormeter(FWD), were summarized and the respective advantages and disadvantages were analyzed. Based on these methods, the evaluation principles were improved and a new type of the neural network, functional-link neural network was proposed to evaluate the sublayer suspension with FWD test results. The concept of function link, learning method of functional-link neural network and the establishment process of neural network model were studied in detail. Based on the old pavement over-repairing engineering of Kaiping section, Guangdong Province in G325 National Highway, the application of functional-link neural network in evaluation of sublayer suspension beneath old pavement based on FWD test data on the spot was investigated. When learning rate is 0.1 and training cycles are 405, the functional-link network error is less than 0.000 1, while the optimum chosen 4-8-1 BP needs over 10 000 training cycles to reach the same accuracy with less precise evaluation results. Therefore, in contrast to common BP neural network,the functional-link neural network adopts single layer structure to learn and calculate, which simplifies the network, accelerates the convergence speed and improves the accuracy. Moreover the trained functional-link neural network can be (adopted) to directly evaluate the sublayer suspension based on FWD test data on the site. Engineering practice indicates that the functional-link neural model gains very excellent results and effectively guides the pavement over-repairing construction.

Complex-Valued Neural Networks:A Comprehensive Survey

Complex-valued neural networks(CVNNs)have shown their excellent efficiency compared to their real counterparts in speech enhancement,image and signal processing.Researchers throughout the years have made many efforts to improve the learning algorithms and activation functions of CVNNs.Since CVNNs have proven to have better performance in handling the naturally complex-valued data and signals,this area of study will grow and expect the arrival of some effective improvements in the future.Therefore,there exists an obvious reason to provide a comprehensive survey paper that systematically collects and categorizes the advancement of CVNNs.In this paper,we discuss and summarize the recent advances based on their learning algorithms,activation functions,which is the most challenging part of building a CVNN,and applications.Besides,we outline the structure and applications of complex-valued convolutional,residual and recurrent neural networks.Finally,we also present some challenges and future research directions to facilitate the exploration of the ability of CVNNs.

Numeral eddy current sensor modelling based on genetic neural network

This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method,the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line modelling and high precision.The maximum nonlinearity error can be reduced to 0.037% by using GNN.However,the maximum nonlinearity error is 0.075% using the least square method.

A New Modeling Method Based on Genetic Neural Network for Numeral Eddy Current Sensor

In this paper,we present a method used to the numeral eddy current sensor modeling based on genetic neural network to settle its nonlinear problem.The principle and algorithms of genetic neural network are introduced.In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data.So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network.The nonlinear model has the advantages of strong robustness,on-line scaling and high precision.The maximum nonlinearity error can be reduced to 0.037% using GNN.However,the maximum nonlinearity error is 0.075% using least square method (LMS).

Multilayer perceptron and Chebyshev polynomials-based functional link artificial neural network for solving differential equations

This paper discusses the issues of computational efforts and the accuracy of solutions of differential equations using multilayer perceptron and Chebyshev polynomials-based functional link artificial neural networks.Some ordinary and partial differential equations have been solved by both these techniques and pros and cons of both these type of feedforward networks have been discussed in detail.Apart from that,various factors that affect the accuracy of the solution have also been analyzed.

Pan evaporation modeling in different agroclimatic zones using functional link artificial neural network

Pan evaporation is an important climatic variable for developing efficient water resource management strategies.In the past,many machine learning models are reported in the literature for pan evaporation modeling using the different combinationof available climatic variables.In order to develop a novel model with improved accuracy and reduced computational complexity,the functional link artificial neural network(FLANN)is chosen as an architecture to estimate daily pan evaporation in three agro-climatic zones(ACZs)of Chhattisgarh state in east-central India.Single neuron and single layer in its structure make it less complex as compared to other multilayer neural networks and neuro-fuzzy based hybrid models.Estimation results obtained with the FLANN model are compared with those obtained by multi-layer artificial neural networks(MLANN)and two empirical methods using the same raw data and corresponding features.Statistical indices like root mean square error(RMSE),mean absolute error(MAE)and efficiency factor(EF)is also computed to evaluate the model performance.It is demonstrated that pan evaporation estimates obtained with the proposed FLANN models provide an improved estimation of pan evaporation(RMSE=0.85 to 1.27 mm d^(-1),MAE=0.63 to 0.95 mm d^(-1) and EF=0.70 to 0.89)as compared to MLANN(RMSE=0.94 to 1.58 mm d^(-1),MAE=0.73 to 1.14 mm d^(-1) and EF=0.62 to 0.88)and empirical(RMSE=1.19 to 2.19 mm d^(-1),MAE=0.91 to 1.62 mm d^(-1) and EF=0.49 to 0.88)models in different ACZs.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer’s disease

Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).

The Module-Phase Synchronization of Complex-Valued Neural Networks with Time-Varying Delay and Stochastic Perturbations

The problem of exponential module-phase synchronization of complex-valued neural networks(CVNNs)with time-varying delay and stochastic perturbations was investigated.The model of CVNNs with time-varying delay and stochastic perturbations was considered.The error system was deduced and the module-phase synchronization was defined.Based on the principle of Lyapunov stability theory,the appropriate controller was designed to control the CVNNs.Finally,the effectiveness and reliability of the method were verified by the numerical simulations.

基于多尺度卷积神经网络和LBP算法的浮选工况识别

针对泡沫浮选加药状态检测困难、识别效率低和主观性强等问题,提出了一种结合多尺-度卷积神经网络(CNN)特征及改进局部二值模式(LBP)计算方法的核随机权神经网络(K RV-FLNs)浮选工况识别方法。首先,对泡沫浮选图像进行非下采样Shearlet多尺度分解,将原始图像分解为不同频率尺度,设计多通道CNN网络对多尺度图像进行特征提取;再通过改进LBP算法提取特征作为补充,将CNN提取的图像特征与LBP特征进行融合;最后,通过核随机权神经网络映射到更高维空间进行分类决策,实现浮选加药状态的精确识别。实验结果表明,采用多尺度CNN及LBP-TOP特征融合的方法识别的精度比传统LBP算法提高了5.34%,比采用单CNN特-征的方法提高了3.76%,结合K RVFLNs实现浮选工况分类准确率高达96.38%,识别精度和稳定性较现有方法有较大提升,且减少了人工干预,有利于提高生产效率。

基于梯度函数的多自由度机械臂关节角度自动控制

在多自由度机械臂运动过程中,易受到多连杆之间耦合作用的影响,导致多自由度机械臂关节角度控制效果和运动轨迹跟踪效果较差。为此,提出了基于梯度函数的多自由度机械臂关节角度自动控制方法。通过分析连杆运动和关节运动,建立多自由度机械臂动力学模型,获取影响多自由度机械臂关节角度的重要因素。在此基础上,基于梯度函数,结合神经网络算法,获取连杆和关节的控制策略,构建关节角度控制模型,实现多自由度机械臂关节角度自动控制。实验结果表明,所提方法能够有效实现多自由度机械臂关节角度自动控制,且具有较好的关节角度控制效果和运动轨迹跟踪效果。

神经网络与计算力矩复合的机器人运动轨迹跟踪控制

为了实现机器人精密运动控制,在其关节系统引入计算力矩法(CTC)与神经网络复合的控制器,旨在通过CTC实现系统的初步控制并利用神经网络补偿机器人的不确定动力学特性所带来的运动误差。首先,建立了机器人的动力学模型并对其不确定性动力学量进行了描述;然后,为机器人构建了双闭环控制系统,并依据机器人标称模型规划出CTC控制律;进而,引入函数链神经网络(FLNN)对不确定性动力学量进行估值,并推导出FLNN的学习律;最后,对系统进行了仿真,结果显示,该复合控制器可将关节位置和速度跟踪误差控制在±0.001 rad和±0.001 rad/s之内,且其对机器人的参数变化及外部扰动具有较强的自适应性与鲁棒性。

基于模糊自适应变权重算法的函数链神经网络预测方法

为提高复杂工业系统非线性时间序列预测精度,将工业系统非线性时间序列不同的单个预测模型预测值作为函数链神经网络的原始输入值,并将原始输入值按正交的三角函数扩展得到的数值作为函数链神经网络扩展输入值,在分析函数链神经网络拟合充要条件的基础上,结合模糊自适应变权重算法计算函数链神经网络权重,建立基于模糊自适应变权重算法的函数链神经网络预测模型。研究结果表明:基于模糊自适应变权重算法的函数链神经网络预测方法的预测精度较高,并且平均误差和预测平方根误差均较小,具有较强的泛化能力;该模糊自适应变权重函数链神经网络预测模型可用于复杂非线性工业系统决策。

基于FLANN的三轴磁强计误差校正研究

提出一种基于函数链接型神经网络(FLANN)的三轴磁强计误差修正方法。由于三轴非正交、灵敏度不一致及零点漂移所引起的误差降低了三轴磁强计的测量精度,因此有必要进行校正。本文先对与三轴磁强计系统参数有关的测量进行详细分析和理论计算;然后,设计矩阵形式的数学模型对该误差进行修正。通过构造相应的FLANN网络结构,实现对模型参数矩阵的辨识。用实际地磁场测量数据进行测试,结果表明,三轴磁强计的转向误差由800 nT修正到12 nT以下。因此,该研究为提高三轴磁强计性能提供了一种可行方法。

传感器动态建模FLANN方法改进研究

提出一种改进的函数连接型神经网络(FLANN),并将其应用于传感器动态建模。首先,将单输入单输出(SISO)的传感器系统表达为动态差分方程模型;再充分考虑动态模型输出的历史值与参数之间的关系,对模型输出与参数的偏导数进行重新推导,得到了对权值参数偏导数的更高精度估计;最后,利用该模型梯度进行迭代训练,加快了网络收敛速度并提高了收敛的稳定性。实验结果表明,改进FLANN具有更快的收敛速度和更强的鲁棒性,十分适合传感器动态系统的建模。

泛函连接网络计算软件及其在生物多样性研究中的应用

针对农田生物多样性分析的需要 ,研制出泛函连接网络 (FLANN)计算软件。该软件由 7个Java类和 1个HTML文件组成 ,是一种Internet在线计算工具 ,可运行于多种操作系统和Web浏览器上 ,并在各种类型的PC及工作站上使用 ,可读取多种类型的数据库文件。对水稻田昆虫生物多样性的两组取样调查数据Zmar18和Zapr15 ,用生物多样性工具软件LUMP和非监督分类 -离差平方和聚类法进行统计归纳及分类 ,分别划分为 2 1个和 2 0个功能群 ,各包含 6 0个样本。以FLANN计算软件对昆虫生物多样性进行了模式分类分析。结果表明 ,泛函连接网络的模式分类及预测与实际测查结果吻合良好。泛函连接网络Internet在线计算软件的应用可促进生物多样性数据采集和分析的规范化 ,有利于数据和信息共享 。

基于FLANN的腕力传感器动态补偿方法

根据“逆模型”的思想,利用神经元网络良好的逼近能力,提出了基于函数联接型神经网络的传感器动态补偿方法。该方法设计的动态补偿器实现简单,实时性好;不依赖于传感器的模型,鲁棒性强;可以优化补偿器的参数。该方法的补偿效果比零极点配置方法的好,是一种非常有效的新方法。